Infinispan eviction, batching updates and LIRS
DataContainer abstraction represents the heart of Infinispan. It is a container structure where actual cache data resides. Every put, remove, get and other invoked cache operations eventually end up in the data container. Therefore, it is of utmost importance the data container is implemented in a way that does not impede overall system throughput. Also recall that the data container’s memory footprint can not grow indefinitely because we would eventually run out of memory; we have to periodically evict certain entries from the data container according to a chosen eviction algorithm.
LRU eviction algorithm, although simple and easy to understand, under performs in cases of weak access locality (one time access entries are not timely replaced, entries to be accessed soonest are unfortunately replaced, and so on). Recently, a new eviction algorithm - LIRS has gathered a lot of attention because it addresses weak access locality shortcomings of LRU yet it retains LRU’s simplicity.
However, no matter what eviction algorithm is utilized, if eviction is not implemented in a scalable, low lock contention approach, it can seriously degrade overall system performance. In order to do any meaningful selection of entries for eviction we have to lock data container until appropriate eviction entries are selected. Having such a lock protected data container in turn causes high lock contention offsetting any eviction precision gained by sophisticated eviction algorithms. In order to get superior throughput while retaining high eviction precision we need both low lock contention data container and high precision eviction algorithm implementation – a seemingly impossible feat.
Instead of making a trade-off between the high precision eviction algorithm and the low lock contention there is a third approach: we keep lock protected data container but we amortize locking cost through batching updates. The basic idea is to wrap any eviction algorithm with a framework that keeps track of cache access per thread (i.e. ThreadLocal) in a simple queue. For each cache hit associated with a thread, the access is recorded in the thread’s queue. If thread’s queue is full or the number of accesses recorded in the queue reaches a certain pre-determined threshold, we acquire a lock and then execute operations defined by the eviction algorithm - once for all the accesses in the queue. A thread is allowed to access many cache items without requesting a lock to run the eviction replacement algorithm, or without paying the lock acquisition cost. We fully exploit a non-blocking lock APIs like tryLock. As you recall tryLock makes an attempt to get the lock and if the lock is currently held by another thread, it fails without blocking its caller thread. Although tryLock is cheap it is not used for every cache access for obvious reasons but rather on certain pre-determined thresholds. In case when thread’s queue is totally full a lock must be explicitly requested. Therefore, using batching updates approach we significantly lower the cost of lock contention, streamline access to locked structures and retain the precision of eviction algorithm such as LIRS. The key insight is that batching the updates on the eviction algorithm doesn’t materially affect the accuracy of the algorithm.
How are these ideas implemented in Infinispan? We introduced BoundedConcurrentHashMap class based on Doug Lea’s ConcurrentHashMap. BoundedConcurrentHashMap hashes entries based on their keys into lock protected segments. Instead of recording entries accessed per thread we record them in a lock free queue on a segment level. The main reason not to use ThreadLocal is that we could potentially have hundreds of threads hitting the data container, some of them very short lived thus possibly never reaching batching thresholds. When predetermined thresholds are reached eviction algorithms is executed on a segment level. Would running eviction algorithm on a segment level rather than entire data container impact overall eviction precision? In our performance tests we have not found any evidence of that.
Infinispan’s eviction algorithm is specified using strategy attribute of eviction XML element. In addition to old eviction approaches, starting with release 4.1.ALPHA2, you can now select LIRS eviction algorithm. LRU remains the default. Also note that starting with 4.1ALPHA2 release there are two distinct approaches to actually evict entries from the cache: piggyback and the default approach using a dedicated EvictionManager thread. Piggyback eviction thread policy, as it name implies, does eviction by piggybacking on user threads that are hitting the data container. Dedicated EvictionManager thread is unchanged from the previous release and it remains the default option. In order to support these two eviction thread policies a new eviction attribute threadPolicy has been added to eviction element of Infinispan configuration schema.
Does eviction redesign based on batching updates promise to live up to its expectations? Ding et al, authors of the original batching proposal, found that their framework increased throughput nearly twofold in comparison with unmodified eviction in postgreSQL 8.2.3. We do not have any numbers to share yet, however, initial testing of BoundedConcurrentHashMap were indeed promising. One of our partner companies replaced their crucial caching component with BoundedConcurrentHashMap and realized a 54% performance improvement on the Berlin SPARQL benchmark for their flagship product. Stay tuned for more updates.
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